---
language:
- en
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
datasets:
- tatsu-lab/alpaca
tags:
- instruction-tuning
- lora
- peft
- trl
- chatbot
- causal-lm
pipeline_tag: text-generation
---

# 🤖 Tiny Chatbot — LoRA Fine-Tuned on Alpaca

A conversational assistant produced by fine-tuning
**[TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)**
on the **[tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)**
instruction dataset (52 K English instruction–response pairs) using
LoRA (rank 16) via TRL's SFTTrainer on a Kaggle Dual T4 GPU environment.

---

## 🚀 Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Havoc999/tiny-chatbot",
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Havoc999/tiny-chatbot")

prompt = (
    "Below is an instruction that describes a task. "
    "Write a response that appropriately completes the request.\n\n"
    "### Instruction:\n"
    "Explain the water cycle in simple terms.\n\n"
    "### Response:\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    repetition_penalty=1.15,
)
response = tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
```

### Multi-turn (Chat Template)

```python
from transformers import pipeline

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

messages = [
    {"role": "user", "content": "What is photosynthesis?"},
]

# TinyLlama-Chat supports the built-in chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(pipe(prompt, max_new_tokens=200)[0]["generated_text"])
```

---

## 📊 Benchmark Results

All benchmarks were evaluated after fine-tuning, using greedy decoding unless otherwise noted.

### MMLU — Elementary Mathematics

| Metric | Value |
|---|---|
| Samples evaluated | 50 |
| Correct | 15 |
| Invalid outputs | 4 |
| **Accuracy** | **30.00%** |
| Random baseline (4-way) | 25.00% |

> **+5 pp above random.** The model demonstrates marginal elementary math ability consistent with the small 1.1 B parameter count and an English instruction dataset that contains limited mathematical content.

---

### HellaSwag *(commonsense NLI)*

| Metric | Score | Samples |
|---|---|---|
| Accuracy | 0.4550 | 200 |
| Accuracy (normalised) | **0.5600** | 200 |

> Normalised accuracy above 0.50 indicates better-than-random commonsense sentence completion. HellaSwag is a strong proxy for general language understanding.

---

### PIQA *(physical intuition QA)*

| Metric | Score | Samples |
|---|---|---|
| Accuracy | 0.7450 | 200 |
| Accuracy (normalised) | **0.7400** | 200 |

> PIQA tests physical intuition and everyday procedural knowledge. 0.74 is a solid result for a 1.1 B model, suggesting the base pre-training retains good world knowledge even after instruction fine-tuning.

---

### ARC Challenge *(grade-school science)*

| Metric | Score | Samples |
|---|---|---|
| Accuracy | 0.3050 | 200 |
| Accuracy (normalised) | **0.3500** | 200 |

> ARC-Challenge targets questions that require reasoning beyond simple retrieval. 0.35 normalised reflects the model's limitations on multi-step reasoning at this scale.

---

### Summary

| Benchmark | Metric | Score |
|---|---|---|
| MMLU Elem. Math | Accuracy | 30.00% |
| HellaSwag | Acc (norm) | 56.00% |
| PIQA | Acc (norm) | 74.00% |
| ARC Challenge | Acc (norm) | 35.00% |

---

## 📋 Training Details

| Setting | Value |
|---|---|
| Base model | TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| Dataset | tatsu-lab/alpaca |
| Train split | 45,000 examples |
| Eval split | 2,000 examples |
| Fine-tuning method | LoRA (PEFT) |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable parameters | ~17 M / 1.1 B (~1.55%) |
| Precision | float16 (AMP) |
| Epochs | 3 |
| Per-GPU batch size | 4 |
| Gradient accumulation | 4 steps |
| Effective global batch | 32 (4 × 2 GPUs × 4 accum) |
| Peak learning rate | 2e-4 |
| LR scheduler | Cosine annealing |
| Warmup ratio | 3% |
| Gradient checkpointing | Enabled |
| NEFTune noise alpha | 5 |
| Hardware | Kaggle Dual T4 (2 × 16 GiB VRAM) |
| Loss masking | Completion-only (response tokens only) |
| Early stopping patience | 3 evaluations |

---

## ⚙️ Reproduce

```python
# Install dependencies
# pip install transformers datasets peft trl accelerate bitsandbytes huggingface_hub

from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
from peft import LoraConfig, get_peft_model, TaskType
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
from datasets import load_dataset

# 1. Load dataset
dataset = load_dataset("tatsu-lab/alpaca", split="train")

# 2. Format examples
def format_alpaca(ex):
    input_section = f"### Input:\n{ex['input']}\n\n" if ex["input"].strip() else ""
    return {
        "text": (
            "Below is an instruction that describes a task. "
            "Write a response that appropriately completes the request.\n\n"
            f"### Instruction:\n{ex['instruction']}\n\n"
            f"{input_section}"
            f"### Response:\n{ex['output']}"
        )
    }

dataset = dataset.map(format_alpaca, batched=False)

# 3. Load model + LoRA
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    torch_dtype="auto",
    device_map={"": 0},
)
model.config.use_cache = False
model.enable_input_require_grads()

lora_config = LoraConfig(
    r=16, lora_alpha=32, lora_dropout=0.05,
    bias="none", task_type=TaskType.CAUSAL_LM,
    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
)
model = get_peft_model(model, lora_config)

# 4. Train
trainer = SFTTrainer(
    model=model, tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=512,
    data_collator=DataCollatorForCompletionOnlyLM("### Response:\n", tokenizer=tokenizer),
    args=TrainingArguments(
        output_dir="./chatbot-lora",
        num_train_epochs=3,
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        learning_rate=2e-4,
        fp16=True,
        gradient_checkpointing=True,
        save_strategy="steps", save_steps=200, save_total_limit=3,
        eval_strategy="no",
    ),
)
trainer.train()
```

---

## ⚠️ Limitations

- **English only** — the base model and Alpaca dataset are English-focused; other languages may produce incoherent outputs.
- **Hallucination** — like all generative models, this one can confidently state incorrect facts. Always verify important claims.
- **Limited reasoning** — at 1.1 B parameters, multi-step logical and mathematical reasoning is unreliable (see ARC / MMLU results above).
- **No RLHF safety alignment** — this model has not undergone reinforcement learning from human feedback. It inherits TinyLlama's base alignment only and may produce inappropriate responses to adversarial prompts.
- **Short context** — trained with a maximum sequence length of 512 tokens; very long conversations will be truncated.
- **Not production-ready** — intended as a learning artefact and research baseline, not a deployed consumer product.

---

## 📜 License

This model is released under the **Apache 2.0** license, consistent with the
[TinyLlama base model](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
and the
[Alpaca dataset](https://huggingface.co/datasets/tatsu-lab/alpaca).
See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for full terms.

---

*Fine-tuned on Kaggle Dual T4 GPU · TRL SFTTrainer · LoRA via PEFT*